diff --git a/pyproject.toml b/pyproject.toml
index 1b830697..cae78492 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,7 +2,7 @@
 name = "supervision"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 license = { text = "MIT" }
-version = "0.26.0"
+version = "0.26.1"
 readme = "README.md"
 requires-python = ">=3.9"
 authors = [
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index efaa366a..ea466b89 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -1939,8 +1939,8 @@ class Detections:
             class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum suppression. If True, the class_id of each detection
                 will be ignored. Defaults to False.
-            overlap_metric (OverlapMetric): Metric used for measuring overlap between
-                detections in slices.
+            overlap_metric (OverlapMetric): Metric used to compute the degree of
+                overlap between pairs of masks or boxes (e.g., IoU, IoS).
 
         Returns:
             Detections: A new Detections object containing the subset of detections
@@ -2003,8 +2003,8 @@ class Detections:
             class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum merging. If True, the class_id of each detection
                 will be ignored. Defaults to False.
-            overlap_metric (OverlapMetric): Metric used for measuring overlap between
-                detections in slices.
+            overlap_metric (OverlapMetric): Metric used to compute the degree of
+                overlap between pairs of masks or boxes (e.g., IoU, IoS).
 
         Returns:
             Detections: A new Detections object containing the subset of detections
diff --git a/supervision/detection/tools/inference_slicer.py b/supervision/detection/tools/inference_slicer.py
index ff4a44b2..aaecccb3 100644
--- a/supervision/detection/tools/inference_slicer.py
+++ b/supervision/detection/tools/inference_slicer.py
@@ -9,7 +9,7 @@ import numpy as np
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
 from supervision.detection.utils.boxes import move_boxes, move_oriented_boxes
-from supervision.detection.utils.iou_and_nms import OverlapFilter
+from supervision.detection.utils.iou_and_nms import OverlapFilter, OverlapMetric
 from supervision.detection.utils.masks import move_masks
 from supervision.utils.image import crop_image
 from supervision.utils.internal import (
@@ -75,8 +75,8 @@ class InferenceSlicer:
             filtering or merging overlapping detections in slices.
         iou_threshold (float): Intersection over Union (IoU) threshold
             used when filtering by overlap.
-        match_metric (str): Metric used for matching detections in slices.
-            "IOU" or "IOS". Defaults "IOU".
+        overlap_metric (Union[OverlapMetric, str]): Metric used for matching detections
+            in slices.
         callback (Callable): A function that performs inference on a given image
             slice and returns detections.
         thread_workers (int): Number of threads for parallel execution.
@@ -96,7 +96,7 @@ class InferenceSlicer:
         overlap_wh: tuple[int, int] | None = None,
         overlap_filter: OverlapFilter | str = OverlapFilter.NON_MAX_SUPPRESSION,
         iou_threshold: float = 0.5,
-        match_metric: str = "IOU",
+        overlap_metric: OverlapMetric | str = OverlapMetric.IOU,
         thread_workers: int = 1,
     ):
         if overlap_ratio_wh is not None:
@@ -112,7 +112,7 @@ class InferenceSlicer:
 
         self.slice_wh = slice_wh
         self.iou_threshold = iou_threshold
-        self.match_metric = match_metric
+        self.overlap_metric = OverlapMetric.from_value(overlap_metric)
         self.overlap_filter = OverlapFilter.from_value(overlap_filter)
         self.callback = callback
         self.thread_workers = thread_workers
@@ -173,11 +173,11 @@ class InferenceSlicer:
             return merged
         elif self.overlap_filter == OverlapFilter.NON_MAX_SUPPRESSION:
             return merged.with_nms(
-                threshold=self.iou_threshold, match_metric=self.match_metric
+                threshold=self.iou_threshold, overlap_metric=self.overlap_metric
             )
         elif self.overlap_filter == OverlapFilter.NON_MAX_MERGE:
             return merged.with_nmm(
-                threshold=self.iou_threshold, match_metric=self.match_metric
+                threshold=self.iou_threshold, overlap_metric=self.overlap_metric
             )
         else:
             warnings.warn(
diff --git a/supervision/detection/utils/iou_and_nms.py b/supervision/detection/utils/iou_and_nms.py
index 56bdff71..1a6f80bc 100644
--- a/supervision/detection/utils/iou_and_nms.py
+++ b/supervision/detection/utils/iou_and_nms.py
@@ -164,7 +164,8 @@ def box_iou_batch(
             `shape = (N, 4)` where `N` is number of true objects.
         boxes_detection (np.ndarray): 2D `np.ndarray` representing detection boxes.
             `shape = (M, 4)` where `M` is number of detected objects.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: Pairwise IoU of boxes from `boxes_true` and `boxes_detection`.
@@ -381,7 +382,8 @@ def _mask_iou_batch_split(
     Args:
         masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
         masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
@@ -433,7 +435,8 @@ def mask_iou_batch(
     Args:
         masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
         masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
         memory_limit (int): memory limit in MB, default is 1024 * 5 MB (5GB).
 
     Returns:
@@ -492,7 +495,8 @@ def mask_non_max_suppression(
             dimensions of each mask.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
         mask_dimension (int): The dimension to which the masks should be
             resized before computing IOU values. Defaults to 640.
 
@@ -543,7 +547,8 @@ def box_non_max_suppression(
             or `(x_min, y_min, x_max, y_max, score, class)`.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: A boolean array indicating which predictions to keep after n
@@ -603,7 +608,8 @@ def _group_overlapping_masks(
             the predictions.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.
@@ -664,7 +670,8 @@ def mask_non_max_merge(
             to use for non-maximum suppression.
         mask_dimension (int): The dimension to which the masks should be
             resized before computing IOU values. Defaults to 640.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: A boolean array indicating which predictions to keep after
@@ -717,7 +724,8 @@ def _group_overlapping_boxes(
             and the confidence scores.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.
@@ -765,7 +773,8 @@ def box_non_max_merge(
             detections of different classes to be merged.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.